plot-methods {ROCR} | R Documentation |
This is the method to plot all objects of class performance.
## S4 method for signature 'performance,missing' plot(x, y, ..., avg="none", spread.estimate="none", spread.scale=1, show.spread.at=c(), colorize=FALSE, colorize.palette=rev(rainbow(256,start=0, end=4/6)), colorkey=colorize, colorkey.relwidth=0.25, colorkey.pos="right", print.cutoffs.at=c(), cutoff.label.function=function(x) { round(x,2) }, downsampling=0, add=FALSE )
x |
an object of class |
y |
not used |
... |
Optional graphical parameters to adjust different components of
the performance plot. Parameters are directed to their target component by
prefixing them with the name of the component
( |
avg |
If the performance object describes several curves
(from cross-validation runs or bootstrap evaluations of one
particular method), the curves from each of the runs can be
averaged. Allowed values are |
spread.estimate |
When curve averaging is enabled, the variation
around the average curve can be visualized as standard error bars
( |
spread.scale |
For |
show.spread.at |
For vertical averaging, this vector determines the x positions for which the spread estimates should be visualized. In contrast, for horizontal and threshold averaging, the y positions and cutoffs are determined, respectively. By default, spread estimates are shown at 11 equally spaced positions. |
colorize |
This logical determines whether the curve(s) should be colorized according to cutoff. |
colorize.palette |
If curve colorizing is enabled, this determines the color palette onto which the cutoff range is mapped. |
colorkey |
If true, a color key is drawn into the 4% border
region (default of |
colorkey.relwidth |
Scalar between 0 and 1 that determines the fraction of the 4% border region that is occupied by the colorkey. |
colorkey.pos |
Determines if the colorkey is drawn vertically at
the |
print.cutoffs.at |
This vector specifies the cutoffs which should be printed as text along the curve at the corresponding curve positions. |
cutoff.label.function |
By default, cutoff annotations along the
curve or at the color key are rounded to two decimal places
before printing. Using a custom |
downsampling |
ROCR can efficiently compute most performance measures even for data sets with millions of elements. However, plotting of large data sets can be slow and lead to PS/PDF documents of considerable size. In that case, performance curves that are indistinguishable from the original can be obtained by using only a fraction of the computed performance values. Values for downsampling between 0 and 1 indicate the fraction of the original data set size to which the performance object should be downsampled, integers above 1 are interpreted as the actual number of performance values to which the curve(s) should be downsampled. |
add |
If |
Tobias Sing tobias.sing@mpi-sb.mpg.de, Oliver Sander osander@mpi-sb.mpg.de
A detailed list of references can be found on the ROCn'COST homepage at http://rocr.bioinf.mpi-sb.mpg.de.
prediction
, performance
,
prediction-class
, performance-class
# plotting a ROC curve: library(ROCR) data(ROCR.simple) pred <- prediction( ROCR.simple$predictions, ROCR.simple$labels ) perf <- performance( pred, "tpr", "fpr" ) plot( perf ) # To entertain your children, make your plots nicer # using ROCR's flexible parameter passing mechanisms # (much cheaper than a finger painting set) par(bg="lightblue", mai=c(1.2,1.5,1,1)) plot(perf, main="ROCR fingerpainting toolkit", colorize=TRUE, xlab="Mary's axis", ylab="", box.lty=7, box.lwd=5, box.col="gold", lwd=17, colorkey.relwidth=0.5, xaxis.cex.axis=2, xaxis.col='blue', xaxis.col.axis="blue", yaxis.col='green', yaxis.cex.axis=2, yaxis.at=c(0,0.5,0.8,0.85,0.9,1), yaxis.las=1, xaxis.lwd=2, yaxis.lwd=3, yaxis.col.axis="orange", cex.lab=2, cex.main=2)